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Record W2068623925 · doi:10.1029/2008jb005804

Correlation between crustal high conductivity zones and seismic activity and the role of carbon during shear deformation

2008· article· en· W2068623925 on OpenAlexafffund
Paul Glover, A. Ádám

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Formations and Processes Exploration
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologyCrustSeismologySeismotectonicsShear (geology)Electrical resistivity and conductivityInduced seismicityMagnetotelluricsShearing (physics)Shear zoneDeformation (meteorology)PetrologyAttenuationGeophysicsGeotechnical engineeringTectonics

Abstract

fetched live from OpenAlex

The electrical conductivity of the lower crust is anomalously high in many locations around the world. Well‐interconnected grain boundary carbon not only has the potential for increasing the electrical conductivity of the rock but also would be expected to reduce its shear strength. We report a new analysis of field observations and new laboratory measurements consistent with deep carbon‐bearing rocks causing observed high conductivities and crustal weaknesses associated with increased seismicity. The field data indicate a correlation between the depths to a zone of high electrical conductivity observed in Transdanubia in Hungary, earthquake focal depths, and zones of high seismic attenuation. The laboratory triaxial deformation experiments show that progressive shearing of a fracture in carbon‐bearing rock can result in a weaker more electrically conductive fracture. These results provide strong evidence for the role of carbon at depth in both electrical conduction and seismotectonics, explaining the correlation between mid‐crustal high reflectivities and high conductivities observed at many locations worldwide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.256
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2008
Admission routes2
Has abstractyes

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